Comparison of several estimators for the covariance of the coefficient matrix
Author(s)
Advisor
Zaman, AsadDate
1995Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
The standard regression analysis assumes that the variances of the disturbance
terms are constant, and the ordinary least squares (OLS) method
employs this very crucial assumption to estimate the covariance of the disturbance
terms perfectly, but OLS fails to estimate well when the variance
of the disturbance terms vary across the observations. A very good method
suggested by Eicker and improved by White to estimate the covariance matrix
of the disturbance terms in case of heteroskedeisticity was proved to be biased.
This paper evaluates the performance of White’s method as well as the OLS
method in several different settings of regression. Furthermore, bootstrapping,
a new method which very heavily depends on computer simulation is included.
Several types of this method are used in several cases of homoskedastic, heteroskedastic,
balanced, and unbalanced regressions.